Supervised Prediction of Remaining Useful Life of Drills from Acoustic Signals - Barbosa, Raphael; Chevtchenko, Sergio; Afshar, Saeed; Ibnul, Naqib; Callou, Gustavo; Andrade, Ermeson (Anais do Workshop de Testes e Tolerância a Falhas (WTF), 2026)
Early failure detection in cutting tools is a critical challenge in automated manufacturing environments, where unplanned interruptions lead to significant operational costs. This study investigates the supervised prediction of imminent drill failures using acoustic signals collected during machining operations. Ultrasonic recordings obtained from microphones positioned inside a Computer Numerical Control (CNC) machine are segmented per hole and labeled according to failure occurrence within different prediction horizons (fail_in_1, fail_in_3, fail_in_5), indicating failures occurring within 1, 3, and 5 future drilling operations, respectively. Two approaches are evaluated: classical machine learning using statistical and spectral features with a Random Forest classifier, and deep learning using Convolutional Neural Networks (CNN) applied to log-Mel spectrograms. To ensure realistic generalization, data splitting is performed at the tool level, preventing leakage between training and test sets. Results show that acoustic signals exhibit discriminative patterns for early failure detection. The CNN achieves higher F1-scores across all horizons, while the Random Forest provides more stable performance and higher AUC values, highlighting the trade-off between sensitivity and robustness in predictive maintenance systems.